Despite achieving human-level performance on many computer vision tasks, deep neural networks are still not as robust as humans towards various distribution shifts [1]}, [2]} including common image corruptions [3]}.
Attempts to understand the vulnerability towards such shifts include analysis of the network architecture [4]},
the features contained in the data [5]}, and frequency analysis of neural networks [6]}, [7]}.
Many approaches have been suggested to improve their robustness to these shifts including approaches based on data augmentations [8]}, [9]}, adversarial training [10]}, [11]}, and pretraining [12]}.
